Autonomous vehicle using path prediction

ABSTRACT

A controller in a host vehicle includes a processor and a memory storing processor-executable instructions. The processor is programmed to calculate an error bound for confidence intervals of a predicted position at a future time of a target vehicle relative laterally to a current position and orientation of the host vehicle at a current time based on a current position and velocity of the target vehicle and the future time.

BACKGROUND

Autonomous vehicles have the ability to operate without the interventionof a human operator, e.g., driver, that is, a vehicle controller makesdecisions about accelerating, braking, and/or steering the vehicle. Avehicle may be fully autonomous or semi-autonomous. A semi-autonomousvehicle may be autonomous only in particular situations, for example,highway driving or parallel parking, or with respect to certain vehiclesubsystems, for example, braking but not acceleration or steering.

An autonomous vehicle may include sensors for tracking an externalenvironment surrounding the vehicle. Some types of sensors are radarsensors, scanning laser range finders, light detection and ranging(LIDAR) devices, and image processing sensors such as cameras. Thevehicle controller is in communication with the sensors and uses outputfrom the sensors to analyze the external environment, for example,defining features of a surrounding landscape, detecting roads and lanesof roads on the landscape, interpreting signs and signals, and trackingand classifying objects in the environment such as vehicles, cyclists,and pedestrians. For example, a vehicle controller may classify whethera detected object is another vehicle and provide state information aboutthe other vehicle, such as location, speed, and heading.

The vehicle controller uses target path prediction to predict whereanother vehicle will travel. The vehicle controller uses the predictedpath of the other vehicle to make decisions affecting operation of thevehicle. Thus, inaccuracies and failures of current path predictiontechnologies are problematic. There is opportunity to improve technologyfor predicting paths of objects such as target vehicles.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a host vehicle.

FIG. 2 is a process flow diagram of a process for implementing targetpath prediction on the host vehicle.

FIG. 3 is a process flow diagram of a process for disabling a feature ofthe host vehicle based on a quality of the target path prediction.

FIG. 4 is a process flow diagram of a process for running a test todetermine the quality of the target path prediction.

FIG. 5 is a process flow diagram of a process for determining thequality of the target path prediction during a test run.

FIG. 6 is a process flow diagram of a process for operating the hostvehicle using target path prediction.

FIG. 7 is a diagram of an interaction between the host vehicle and atarget vehicle.

DETAILED DESCRIPTION

With reference to the Figures, wherein like numerals indicate like partsthroughout the several views, a controller 34 in a host vehicle 30includes a processor and a memory storing processor-executableinstructions. The processor is programmed to calculate an error boundfor confidence intervals of a predicted position at a future time of atarget vehicle 32 relative laterally to a current position andorientation of the host vehicle 30 at a current time based on a currentposition and velocity of the target vehicle 32 and the future time.

Use of the error bound makes target path prediction, and henceautonomous operation of the host vehicle 30, more robust because thecontroller 34 makes decisions based on a range of the most likelypredicted positions for the target vehicle 32. Furthermore, calculationof the error bound is adaptive in that the calculation updatescontinuously as the host vehicle 30 travels.

With reference to FIG. 1, the host vehicle 30 may be an autonomousvehicle. The controller 34, sometimes referred to as the “virtualdriver,” may be capable of operating the vehicle independently of theintervention of a human driver, to a greater or a lesser degree, i.e.,fully autonomously and/or in a semi-autonomous mode. The controller 34may be programmed to operate a steering system 40, a propulsion 42,brakes 44, and/or other vehicle systems. For purposes of thisdisclosure, fully autonomous means that each of the steering system 40,the propulsion 42, and the brakes 44 of the host vehicle 30 arecontrolled by the controller 34; a semi-autonomous mode is one in whichone or two of the steering system 40, the propulsion 42, and the brakes44 are operated by the controller 34.

The controller 34 carries out various operations, including as describedherein. The controller 34 is a computing device that generally includesa processor and a memory, the memory including one or more forms ofcomputer-readable media, and storing instructions executable by theprocessor for performing various operations, including as disclosedherein. The memory of the controller 34 further generally stores remotedata received via various communications mechanisms; e.g., thecontroller 34 is generally configured for communications on a controllerarea network (CAN) bus or the like, and/or for using other wired orwireless protocols, e.g., Bluetooth, etc. The controller 34 may alsohave a connection to an onboard diagnostics connector (OBD-II). Via avehicle network using Ethernet, WiFi, the CAN bus, Local InterconnectNetwork (LIN), and/or other wired or wireless mechanisms, the controller34 may transmit messages to various devices in the vehicle and/orreceive messages from the various devices, e.g., controllers, actuators,sensors, etc., e.g., controllers and sensors as discussed herein. Forexample, the controller 34 may receive data from sensors 36. Althoughone controller 34 is shown in FIG. 1 for ease of illustration, it is tobe understood that the controller 34 could include, and variousoperations described herein could be carried out by, one or morecomputing devices.

The controller 34 may transmit signals through a communications network38 such as a controller area network (CAN) bus, Ethernet, LocalInterconnect Network (LIN), and/or by any other wired or wirelesscommunications network. The controller 34 may be in communication withthe sensors 36, the steering system 40, the propulsion 42, the brakes44, and other vehicle subsystems and/or features.

The host vehicle 30 may include the sensors 36. The sensors 36 maydetect internal states of the vehicle, for example, wheel speed, wheelorientation, and engine and transmission variables. The sensors 36 maydetect the position or orientation of the vehicle, for example, globalpositioning system (GPS) sensors; accelerometers such as piezo-electricor microelectromechanical systems (MEMS); gyroscopes such as rate, ringlaser, or fiber-optic gyroscopes; inertial measurements units (IMU); andmagnetometers. The sensors 36 may detect an environment external to abody of the host vehicle 30, for example, the sensors 36 can include oneor more of radar sensors, scanning laser range finders, light detectionand ranging (LIDAR) devices, and image processing sensors such ascameras. The sensors 36 may include communications devices, for example,vehicle-to-infrastructure (V2I) or vehicle-to-vehicle (V2V) devices.

The steering system 40 is typically a known vehicle steering subsystemand controls the turning of wheels of the host vehicle 30. The steeringsystem 40 is in communication with and receives input from a steeringwheel and the controller 34. The steering system 40 may be arack-and-pinion system with electric power-assisted steering, asteer-by-wire system, as are both known in the art, or any othersuitable system.

The propulsion 42 of the host vehicle 30 generates energy and translatesthe energy into motion of the host vehicle 30. The propulsion 42 may bea known vehicle propulsion subsystem, for example, a conventionalpowertrain including an internal-combustion engine coupled to atransmission that transfers rotational motion to wheels; an electricpowertrain including batteries, an electric motor, and a transmissionthat transfers rotational motion to the wheels; a hybrid powertrainincluding elements of the conventional powertrain and the electricpowertrain; or any other type of propulsion. The propulsion 42 is incommunication with and receives input from the controller 34 and from ahuman driver. The human driver may control the propulsion 42 via, e.g.,an accelerator pedal and/or a gear-shift lever.

The brakes 44 are typically a known vehicle braking subsystem and areprovided to resist the motion of the host vehicle 30 to thereby slowand/or stop the vehicle. The brakes 44 may be friction brakes such asdisc brakes, drum brakes, band brakes, and so on; regenerative brakes;any other suitable type of brakes; or a combination. The brakes 44 arein communication with and receive input from the controller 34 and ahuman driver. The human driver may control the brakes 44 via, e.g., abrake pedal.

With reference to FIG. 7, the exemplary processes described belowpertain to target path prediction. The host vehicle 30 may interact withthe target vehicle 32 while each are traveling. “Target path prediction”means predicting, by the host vehicle 30, a path P along which thetarget vehicle 32 will travel. An actual path A by the target vehicle 32may differ from the predicted path P. Motion variables shown in FIG. 7describing the travel of the vehicles 30, 32 are described below withrespect to a block 415 in a process 400.

FIG. 2 is a process flow diagram illustrating an exemplary process 200for implementing target path prediction on the host vehicle 30. Theprocess 200 may be used by, for example, a system designer testing andimplementing an algorithm for target path prediction on a type ofvehicle. The process 200 begins by performing an exemplary process 400for running a test to determine the quality of the target pathprediction, shown in FIG. 4 and described below. The process 400 returnsa quality measure Q of the target path prediction to the controller 34.

Next, after receiving input from the process 400, in a decision block205, the controller 34 determines whether the quality measure Q is abovea quality threshold Q_(th). The quality threshold is a value chosen toensure a sufficiently robust performance of the target path prediction.

If the quality measure Q is not above the quality threshold Q_(th),next, in a block 210, a system designer adjusts a path predictionalgorithm for target path prediction based on the quality measure Q. Thesystem designer may adjust parameters for the target path predictionalgorithm, such as the quadratic polynomial method, as is known(described below with respect to a block 420 of the process 400 and ablock 515 of a process 500). Alternatively or additionally, the systemdesigner may adjust parameters for the error bounds aspect of the pathprediction algorithm (described below with respect to a block 525 of theprocess 500), for example, a lateral offset bound x _(targ) for thetarget vehicle 32, a longitudinal offset bound y _(targ) for the targetvehicle 32, a location bound b for the target vehicle 32, a velocitybound v _(targ) for the target vehicle 32, a velocity bound v _(host)for the host vehicle 30, a yaw rate bound w _(host) for the host vehicle30, and/or a default initialization values σ_(x) _(targ) , σ_(y) _(targ), σ_(v) _(targ) , σ_(v) _(host) , and σ_(w) _(host) .

If, in the decision block 205, the quality measure Q is above thequality threshold Q_(th), the controller 34 performs an exemplaryprocess 600 for operating the host vehicle 30 using target pathprediction, as shown in FIG. 6 and described below. After the process600, the process 200 ends.

FIG. 3 is a process flow diagram illustrating an exemplary process 300for disabling a feature of the host vehicle 30 based on a quality of thetarget path prediction. The process 300 may be performed, for example,by the controller 34 in the host vehicle 30. The process 300 begins byperforming the exemplary process 400 for running a test to determine thequality of the target path prediction, shown in FIG. 4 and describedbelow. The process 400 returns the quality measure Q of the target pathprediction to the controller 34.

Next, after receiving input from the process 400, in a decision block305, the controller 34 determines whether the quality measure Q is abovea quality threshold Q_(th), as described above with respect to thedecision block 205.

If the quality measure Q is not above the quality threshold Q_(th),next, in a block 310, the controller 34 disables a feature relying on anerror bound ε (described below with respect to the block 525 of theprocess 500) based on the quality measure Q. Features that may rely onthe error bound ε may include autonomous or semi-autonomous operation ofthe host vehicle 30, that is, operation of one or more of the steeringsystem 40, propulsion 42, and brakes 44, such as automated braking orautomated steering. Other features that may rely on the error bound εmay include features that may anticipate movement of the host vehicle30, for example, automatically turning headlights.

If, in the decision block 305, the quality measure Q is above thequality threshold Q_(th), the controller 34 performs the exemplaryprocess 600 for operating the host vehicle 30 using target pathprediction, as shown in FIG. 6 and described below. After the process600, the process 300 ends.

FIG. 4 is a process flow diagram illustrating the exemplary process 400for running a test to determine the quality of the target pathprediction. The process 400 begins in a block 405, in which thecontroller 34 initializes an index value k to 1. The index value kcounts a number of test runs each of a duration equal to a test-run timelimit T_(M).

Next, in a block 410, the controller 34 performs the kth test run.Performing the test run involves driving the host vehicle 30 for aduration T_(M) from an initial time t₀ to a final time t₀+T_(M). Drivingthe host vehicle 30 may be performed autonomously by the controller 34,semi-autonomously by the controller 34 and a human driver, or manuallyby the human driver. The block 410 begins an experiment cycle throughblocks 410 to 430 of a number K of test runs. The number K of test runsis a preset or user-specified value and may be chosen, e.g., to providea sufficient period to gather a statistically useful set of data, asdescribed below.

Next, during the test run, in a block 415, the controller 34 measures aset of motion variables for the host vehicle 30 and the target vehicle32. The motion variables may be measured in a known coordinate systemsuch as an objective over-the-ground coordinate system, ahost-vehicle-centered coordinate system, or any other suitablecoordinate system, and the motion variables may be converted from onecoordinate system to another before further use. For example, thecontroller 34 may measure this set of motion variables inover-the-ground coordinates:

{X_(targ)^(M)(t), Y_(targ)^(M)(t), ψ_(targ)^(M)(t), v_(targ)^(M)(t), a_(targ)^(M)(t)}_(t₀ ≤ t ≤ t₀ + T_(M)){X_(host)^(M)(t), Y_(host)^(M)(t), ψ_(host)^(M)(t), v_(host)^(M)(t), a_(host)^(M)(t)}_(t₀ ≤ t ≤ t₀ + T_(M))

in which the subscript host denotes a variable related to the hostvehicle 30, the subscript targ denotes a variable related to the targetvehicle 32, the superscript M denotes a measured variable, X denotes anover-the-ground X-coordinate, Y denotes an over-the-ground Y-coordinate,ψ denotes an over-the-ground heading angle, v denotes a speed, and adenotes an acceleration. The over-the-ground motion variables may thenbe converted into the host-vehicle-centered coordinate system:

{x_(targ)^(M)(t), y_(targ)^(M)(t), v_(targ)^(M)(t), a_(targ)^(M)(t), w_(host)^(M)(t), v_(host)^(M)(t), a_(host)^(M)(t), a^(M)(t)}_(t₀ ≤ t ≤ t₀ + T_(M))

in which x denotes a lateral position relative to the host vehicle 30,that is, a host-vehicle-centered X-coordinate; y denotes a longitudinalposition relative to the host vehicle 30, that is, ahost-vehicle-centered Y-coordinate; w denotes a yaw rate; and a denotesa relative heading angle between the host vehicle 30 and the targetvehicle 32. The motion variables in the host-vehicle-centered coordinatesystem are shown in FIG. 7. The motion variables continue to be recordedas the host vehicle 30 moves and as the target vehicle 32 moves along anactual path A.

Next, during the test run, in the block 420, the controller 34 generatescoefficients to calculate the predicted position of the target vehicle32 at the future time using a quadratic polynomial method. A predictedpath P, which is a sequence of predicted positions of the target vehicle32 in the host-vehicle-centered coordinate system fixed at time t, isestimated as a second-order polynomial:

x _(targ) ^(P) =a ₀(t)+a ₁(t)*y _(targ) ^(P) +a ₂(t)*(y _(targ) ^(P))²

in which the superscript P denotes a predicted variable; and a₀(t),a₁(t), and a₂(t) are the coefficients generated by the quadraticpolynomial method, as is known. Another method of target path predictionbesides the quadratic polynomial method, such as a Kalman filter-basedmotion-prediction method, may be used.

Next, during the test run, the controller 34 performs an exemplaryprocess 500 for determining the quality of the target path predictionduring the test run, as shown in FIG. 5 and described below. The process500 returns a quality measure Q_(test) of the target path predictionduring the test run to the controller 34.

Next, after receiving input from the process 500, in a block 425, thecontroller 34 increments the index value k by 1, so the index value k isequal to k+1.

Next, in a decision block 430, the controller 34 determines whether theindex value k has reached or exceeded the number K of test runs. If theindex value k is still below the number K of test runs, then the process400 proceeds back to the block 410, and the controller 34 performsanother loop of the experiment cycle of the blocks 410 to 430 with theupdate index value k from the block 425.

If the index value k is greater than or equal to the number K of testruns, next, in a block 435, the controller 34 calculates the qualitymeasure Q equal to a minimum value during the period of the experimentof a lesser value of 1 and a ratio of the error bound ε and the errormetric δ, as described below with respect to the process 500. In otherwords, the controller 34 calculates the lowest minimum itemized qualitymeasure Q_(test):

$Q = {\min\limits_{k \in {\lbrack{1,K}\rbrack}}Q_{test}}$

After the block 435, the process 400 ends.

FIG. 5 is a process flow diagram illustrating the exemplary process 500for determining the quality of the target path prediction during thetest run. The process 500 begins in a block 505, in which the controller34 initializes a time variable t to an initial value t₀.

Next, in a block 510, the controller 34 initializes a prediction timevariable s to the value of the time variable t. The time variable ttracks the current time, and the prediction time variable s tracks thefuture time for predictions. The block 510 begins a test-run cyclethrough blocks 510 to 550 for a duration equal to the test-run timelimit T_(M). The test-run time limit T_(M) is a preset value and may bechosen, e.g., to provide a sufficient period to gather a statisticallyuseful set of data, as described below.

Next, in a block 515, the controller 34 calculates the predictedposition (x_(targ) ^(P))(s), y_(targ) ^(P)(s)) of the target vehicle 32at the future time s. The controller 34 may use known methods, includingthe quadratic polynomial method using the coefficients determined above.For example, the controller 34 may use this set of equations:

  x_(targ)^(P)(s) = a₀(t) + a₁(t) * y_(targ)^(P)(s) + a₂(t) * (y_(targ)^(P)(s))²${y_{targ}^{P}(s)} = {{y_{targ}^{M}(t)} + {{v_{targ}^{M}(t)}*{\sin \left( {{a^{M}(t)} + \frac{\pi}{2}} \right)}*\left( {s - t} \right)} + {\frac{1}{2}{a_{targ}^{M}(t)}*{\sin \left( {{a^{M}(t)} + \frac{\pi}{2}} \right)}*\left( {s - t} \right)^{2}}}$

Alternatively, the controller 34 may only calculate the predictedlateral position x_(targ) ^(P)(s) relative to the host vehicle 30. Theblock 515 begins a prediction cycle through blocks 515 to 540 for aduration equal to a prediction time limit T_(P). The prediction timelimit T_(P) is a preset value and may depend on, e.g., a sensitivity ofthe sensors 36 or a processing speed of the controller 34.

Next, in a block 520, the controller 34 calculates an error metric δequal to an absolute value of a difference of the predicted position anda measured position at the future time of the target vehicle 32 relativelaterally to the current position and orientation of the host vehicle30, as described in the following equation:

δ(t, s)=|x _(targ) ^(M)(s)−x _(targ) ^(P)(s)|

If the quadratic polynomial method is used, then the error metric δbecomes:

δ(t, s)=|x _(targ) ^(M)(s)−(a ₀(t)+a ₁(t)*y_(targ) ^(M)(s)+a ₂(t)*(y_(targ) ^(M)(s))²)|

In both equations for the error metric δ, the measured position(x_(targ) ^(M)(s), y_(targ) ^(M)(s)) is measured at the prediction times in the host-vehicle-centered coordinate system fixed at time t.

Next, in the block 525, the controller 34 calculates the error bound εfor confidence intervals of the predicted position at the future time ofthe target vehicle 32 relative laterally to the current position andorientation of the host vehicle 30 at the current time based on thecurrent position and velocity of the target vehicle 32 and the futuretime. A position of the target vehicle 32 “relative laterally to” aposition of the host vehicle 30 refers to the lateral position xrelative to the host vehicle 30, that is, the host-vehicle-centeredX-coordinate. The error bound may be further based on the currentvelocity and yaw rate of the host vehicle 30 and the relative headingangle between the host and target vehicles 30, 32. The error bound ε mayhave a linear relationship with the future time s. The error bound ε maybe determined from this equation:

ε(t, s)=b(t)*(s−t)+c(t)

in which b(t) and c(t) are control variables for the error bound ε. Thecontrol variables b(t) and c(t) are determined from these equationsusing the motion variables as inputs:

${b(t)} = {\frac{1}{1 + {\cos \left( {a^{M}(t)} \right)}}*\left( \frac{{{x_{targ}^{M}(t)}} + \sigma_{x_{targ}}}{{\overset{\_}{x}}_{targ}} \right)*\left( \frac{{{y_{targ}^{M}(t)}} + \sigma_{y_{targ}}}{{\overset{\_}{y}}_{targ}} \right)*\left( \frac{{{v_{targ}^{M}(t)}} + \sigma_{v_{targ}}}{{\overset{\_}{v}}_{targ}} \right)*\left( \frac{{{v_{host}^{M}(t)}} + \sigma_{v_{host}}}{{\overset{\_}{v}}_{host}} \right)*\left( \frac{{{w_{host}^{M}(t)}} + \sigma_{w_{host}}}{{\overset{\_}{w}}_{host}} \right)}$$\mspace{20mu} {{c(t)} = \sqrt{\frac{\left( {x_{targ}^{M}(t)} \right)^{2} + \left( {y_{targ}^{M}(t)} \right)^{2}}{\overset{\_}{b}}}}$

in which ε is the error bound; t is the current time; s is the futuretime; (x_(targ) ^(M)(t), y_(targ) ^(M)(t)) is the current position ofthe target vehicle 32 in a host-vehicle-centered coordinate system;v_(targ) ^(M)(t) is the current velocity of the target vehicle 32;v_(host) ^(M)(t) is the current velocity of the host vehicle 30;w_(host) ^(M)(t) is the current yaw rate of the host vehicle 30;α^(M)(t) is the relative heading angle between the host and targetvehicles 30, 32; x _(targ) is a lateral offset bound for the targetvehicle 32; y _(targ) is a longitudinal offset bound for the targetvehicle 32; b is a location bound for the target vehicle 32; v _(targ)is a velocity bound for the target vehicle 32; v _(host) is a velocitybound for the host vehicle 30; W _(host) is a yaw rate bound for thehost vehicle 30; and σ_(x) _(targ) , σ_(y) _(targ) , σ_(v) _(targ) ,σ_(v) _(host) , and σ_(w) _(host) are default initialization values. Ascalculated in the above equation, the error bound ε is based only oninputs consisting of the current position and velocity of the targetvehicle 32, the future time, the current velocity and yaw rate of thehost vehicle 30, and the relative heading angle between the host andtarget vehicles 30, 32.

Next, in a block 530, the controller 34 calculates an itemized qualitymeasure Q_(item) for the prediction time s. The itemized quality measureQ_(item) is equal to the lesser of 1 and a ratio of the error bound εand the error metric δ; in other words, the itemized quality measureQ_(item) is equal to 1 if the error bound ε is at least as great as theerror metric δ:

${Q_{item}\left( {t,s} \right)} = \left\{ \begin{matrix}1 & {{{if}\mspace{14mu} {\delta \left( {t,s} \right)}} \leq {ɛ\left( {t,s} \right)}} \\\frac{ɛ\left( {t,s} \right)}{\delta \left( {t,s} \right)} & {otherwise}\end{matrix} \right.$

Next, in a block 535, the controller 34 increments the prediction time sby an incremental prediction time step ΔT_(P). In other words, theprediction time s is changed to s+ΔT_(P). The incremental predictiontime step ΔT_(P) is a preset value for a duration of each loop of theprediction cycle. The incremental prediction time step ΔT_(P) may dependon, e.g., a sensitivity of the sensors 36 or a processing speed of thecontroller 34.

Next, in a decision block 540, the controller 34 determines whether theprediction cycle has not yet reached the prediction time limit T_(P). Inother words, the controller 34 determines whether the prediction time sis less than or equal to t+T_(P). If the prediction time s is less thanor equal to t+T_(P), then the process 500 proceeds back to the block515, and the controller 34 performs another loop of the prediction cycleof the blocks 515 to 540 with the same time t and the updated predictiontime s from the block 535.

If the prediction time s is greater than t+T_(P), next, in a block 545,the controller 34 increments the time t by the incremental time stepΔt_(M). In other words, the time t is changed to t+Δt_(M). Theincremental prediction time step Δt_(M) is a preset value for a durationof each loop of the test-run cycle. The incremental prediction time stepΔt_(M) may depend on, e.g., a sensitivity of the sensors 36 or aprocessing speed of the controller 34.

Next, in a decision block 550, the controller 34 determines whether thetest-run cycle has not yet reached the test-run time limit T_(M). Inother words, the controller 34 determines whether the time t is lessthan or equal to t₀+T_(M). If the time t is less than or equal tot₀+T_(M), then the process proceeds back to the block 510, and thecontroller 34 performs another loop of the test-run cycle of the blocks510 to 550 with the updated time t from the block 545.

If the time t is greater than t₀+T_(M), next, in a block 555, thecontroller 34 calculates the minimum itemized quality measure Q_(test),which is the lowest itemized quality measure Q_(item) over the test run:

$Q_{test} = {\min\limits_{t \in {\lbrack{0,{t + T_{M}}}\rbrack}}\left( {\min\limits_{s \in {\lbrack{t,{t + T_{P}}}\rbrack}}{Q_{item}\left( {t,s} \right)}} \right)}$

After the block 555, the process 500 ends.

FIG. 6 is a process flow diagram illustrating the exemplary process 600for operating the host vehicle 30 using target path prediction. Theprocess 600 begins in a block 605. The controller 34 measures a set ofmotion variables for the host vehicle 30 and the target vehicle 32, asdescribed above with respect to the block 415.

Next, in a block 610, the controller 34 generates coefficients tocalculate the predicted position of the target vehicle 32 at the futuretime using a quadratic polynomial method, as described with respect tothe block 420.

Next, in a block 615, the controller 34 initializes the prediction times to the value of the time t.

Next, in a block 620, the controller 34 calculates the predictedposition (x_(targ) ^(P)(s), y_(targ) ^(P)(s)) of the target vehicle 32at the future time s, as described above with respect to the block 515.

Next, in a block 625, the controller 34 calculates an error bound ε forconfidence intervals of the predicted position (x_(targ) ^(P)(s),y_(targ) ^(P)(s)) at the future time s of the target vehicle 32 relativelaterally to the current position and orientation of the host vehicle 30at the current time, as described above with respect to the block 525.

Next, in a block 630, the controller 34 calculates an error-boundedpredicted position (x_(targ) ^(P,err)(s), y_(targ) ^(P,err)(s)) at thefuture time s. Specifically, the controller 34 may apply the error boundε to the lateral predicted position x_(targ) ^(P)(s) relative to thehost vehicle 30. The error-bounded predicted position is given by theseequations:

$\quad\left\{ \begin{matrix}{{x_{targ}^{P,{err}}(s)} = {{x_{targ}^{P}(s)} \pm {ɛ\left( {t,s} \right)}}} \\{{y_{targ}^{P,{err}}(s)} = {y_{targ}^{P}(s)}}\end{matrix} \right.$

Next, in a block 635, the controller 34 determines an autonomousoperation to perform for driving the host vehicle 30 based on theerror-bounded predicted position, that is, based on the predictedposition and the error bound. An “autonomous operation” is an action byone or more of the steering system 40, the propulsion 42, and the brakes44 upon instruction by the controller 34, for example, activating thebrakes 44 with a given force for a given period of time, changing asteering angle by a given number of degrees, etc. Specifically, if thehost vehicle 30 may cross a region specified by the error-boundedpredicted position, then the controller 34 may control a vehiclesubsystem to evade the target vehicle 32 based on the predicted positionand the error bound. Vehicle subsystems include, for example, thesteering system 40, propulsion 42, and brakes 44. Controlling thevehicle subsystem may include the steering system 40 to turn,instructing the propulsion 42 to accelerate, and/or instructing thebrakes 44 to brake.

Next, in a block 640, the controller 34 increments the prediction time sby the incremental prediction time step ΔT_(P), as described above withrespect to the block 535.

Next, in a decision block 645, the controller 34 determines whether theprediction cycle has not yet reached the prediction time limit T_(P). Inother words, the controller 34 determines whether the prediction time sis less than or equal to t+T_(P). If the prediction time s is less thanor equal to t+T_(P), then the process 600 proceeds back to the block620, and the controller 34 performs another loop of the blocks 620 to645 with the same time t and the updated prediction time s from theblock 640. If the prediction time s is greater than t+T_(P), then theprocess 600 ends.

In general, the computing systems and/or devices described may employany of a number of computer operating systems, including, but by nomeans limited to, versions and/or varieties of the Ford Sync®application, AppLink/Smart Device Link middleware, the MicrosoftAutomotive® operating system, the Microsoft Windows® operating system,the Unix operating system (e.g., the Solaris® operating systemdistributed by Oracle Corporation of Redwood Shores, Calif.), the AIXUNIX operating system distributed by International Business Machines ofArmonk, N.Y., the Linux operating system, the Mac OSX and iOS operatingsystems distributed by Apple Inc. of Cupertino, Calif., the BlackBerryOS distributed by Blackberry, Ltd. of Waterloo, Canada, and the Androidoperating system developed by Google, Inc. and the Open HandsetAlliance, or the QNX® CAR Platform for Infotainment offered by QNXSoftware Systems. Examples of computing devices include, withoutlimitation, an on-board vehicle computer, a computer workstation, aserver, a desktop, notebook, laptop, or handheld computer, or some othercomputing system and/or device.

Computing devices generally include computer-executable instructions,where the instructions may be executable by one or more computingdevices such as those listed above. Computer executable instructions maybe compiled or interpreted from computer programs created using avariety of programming languages and/or technologies, including, withoutlimitation, and either alone or in combination, Java™, C, C++, Matlab,Simulink, Stateflow, Visual Basic, Java Script, Perl, HTML, etc. Some ofthese applications may be compiled and executed on a virtual machine,such as the Java Virtual Machine, the Dalvik virtual machine, or thelike. In general, a processor (e.g., a microprocessor) receivesinstructions, e.g., from a memory, a computer readable medium, etc., andexecutes these instructions, thereby performing one or more processes,including one or more of the processes described herein. Suchinstructions and other data may be stored and transmitted using avariety of computer readable media. A file in a computing device isgenerally a collection of data stored on a computer readable medium,such as a storage medium, a random access memory, etc.

A computer-readable medium (also referred to as a processor-readablemedium) includes any non-transitory (e.g., tangible) medium thatparticipates in providing data (e.g., instructions) that may be read bya computer (e.g., by a processor of a computer). Such a medium may takemany forms, including, but not limited to, non-volatile media andvolatile media. Non-volatile media may include, for example, optical ormagnetic disks and other persistent memory. Volatile media may include,for example, dynamic random access memory (DRAM), which typicallyconstitutes a main memory. Such instructions may be transmitted by oneor more transmission media, including coaxial cables, copper wire andfiber optics, including the wires that comprise a system bus coupled toa processor of a ECU. Common forms of computer-readable media include,for example, a floppy disk, a flexible disk, hard disk, magnetic tape,any other magnetic medium, a CD-ROM, DVD, any other optical medium,punch cards, paper tape, any other physical medium with patterns ofholes, a RAM, a PROM, an EPROM, a FLASH-EEPROM, any other memory chip orcartridge, or any other medium from which a computer can read.

Databases, data repositories or other data stores described herein mayinclude various kinds of mechanisms for storing, accessing, andretrieving various kinds of data, including a hierarchical database, aset of files in a file system, an application database in a proprietaryformat, a relational database management system (RDBMS), etc. Each suchdata store is generally included within a computing device employing acomputer operating system such as one of those mentioned above, and areaccessed via a network in any one or more of a variety of manners. Afile system may be accessible from a computer operating system, and mayinclude files stored in various formats. An RDBMS generally employs theStructured Query Language (SQL) in addition to a language for creating,storing, editing, and executing stored procedures, such as the PL/SQLlanguage mentioned above.

In some examples, system elements may be implemented ascomputer-readable instructions (e.g., software) on one or more computingdevices (e.g., servers, personal computers, etc.), stored on computerreadable media associated therewith (e.g., disks, memories, etc.). Acomputer program product may comprise such instructions stored oncomputer readable media for carrying out the functions described herein.

In the drawings, the same reference numbers indicate the same elements.Further, some or all of these elements could be changed. With regard tothe media, processes, systems, methods, heuristics, etc. describedherein, it should be understood that, although the steps of suchprocesses, etc. have been described as occurring according to a certainordered sequence, such processes could be practiced with the describedsteps performed in an order other than the order described herein. Itfurther should be understood that certain steps could be performedsimultaneously, that other steps could be added, or that certain stepsdescribed herein could be omitted. In other words, the descriptions ofprocesses herein are provided for the purpose of illustrating certainembodiments, and should in no way be construed so as to limit theclaims.

Accordingly, it is to be understood that the above description isintended to be illustrative and not restrictive. Many embodiments andapplications other than the examples provided would be apparent to thoseof skill in the art upon reading the above description. The scope of theinvention should be determined, not with reference to the abovedescription, but should instead be determined with reference to theappended claims, along with the full scope of equivalents to which suchclaims are entitled. It is anticipated and intended that futuredevelopments will occur in the arts discussed herein, and that thedisclosed systems and methods will be incorporated into such futureembodiments. In sum, it should be understood that the invention iscapable of modification and variation and is limited only by thefollowing claims.

All terms used in the claims are intended to be given their plain andordinary meanings as understood by those skilled in the art unless anexplicit indication to the contrary in made herein. In particular, useof the singular articles such as “a,” “the,” “said,” etc. should be readto recite one or more of the indicated elements unless a claim recitesan explicit limitation to the contrary.

The disclosure has been described in an illustrative manner, and it isto be understood that the terminology which has been used is intended tobe in the nature of words of description rather than of limitation. Manymodifications and variations of the present disclosure are possible inlight of the above teachings, and the disclosure may be practicedotherwise than as specifically described.

What is claimed is:
 1. A controller comprising a processor and a memorystoring processor-executable instructions, wherein the processor isprogrammed to: calculate an error bound for confidence intervals of apredicted position at a future time of a target vehicle relativelaterally to a current position and orientation of a host vehicle at acurrent time based on a current position and velocity of the targetvehicle and the future time.
 2. The controller of claim 1, wherein theerror bound is further based on a current velocity and yaw rate of thehost vehicle.
 3. The controller of claim 2, wherein the error bound isfurther based on a relative heading angle between the host and targetvehicles.
 4. The controller of claim 3, wherein the error bound has alinear relationship with the future time.
 5. The controller of claim 4,wherein the error bound is determined from an equation: ε(t,s)=b(t)*(s−t)+c(t), in which${{b(t)} = {\frac{1}{1 + {\cos \left( {a^{M}(t)} \right)}}*\left( \frac{{{x_{targ}^{M}(t)}} + \sigma_{x_{targ}}}{{\overset{\_}{x}}_{targ}} \right)*\left( \frac{{{y_{targ}^{M}(t)}} + \sigma_{y_{targ}}}{{\overset{\_}{y}}_{targ}} \right)*\left( \frac{{{v_{targ}^{M}(t)}} + \sigma_{v_{targ}}}{{\overset{\_}{v}}_{targ}} \right)*\left( \frac{{{v_{host}^{M}(t)}} + \sigma_{v_{host}}}{{\overset{\_}{v}}_{host}} \right)*\left( \frac{{{w_{host}^{M}(t)}} + \sigma_{w_{host}}}{{\overset{\_}{w}}_{host}} \right)}},{and}$$\mspace{20mu} {{{c(t)} = \sqrt{\frac{\left( {x_{targ}^{M}(t)} \right)^{2} + \left( {y_{targ}^{M}(t)} \right)^{2}}{\overset{\_}{b}}}};}$in which ε is the error bound, t is the current time, s is the futuretime, (x_(targ) ^(M)(t), y_(targ) ^(M)(t)) is the current position ofthe target vehicle in a host-vehicle-centered coordinate system,v_(targ) ^(M)(t) is the current velocity of the target vehicle, v_(host)^(M)(t) is the current velocity of the host vehicle, w_(host) ^(M)(t) isthe current yaw rate of the host vehicle, α^(M)(t) is the relativeheading angle between the host and target vehicles, x _(targ) is alateral offset bound for the target vehicle, y _(targ) is a longitudinaloffset bound for the target vehicle, b is a location bound for thetarget vehicle, v _(targ) is a velocity bound for the target vehicle, v_(host) is a velocity bound for the host vehicle, w _(host) is a yawrate bound for the host vehicle, and σ_(x) _(targ) , σ_(y) _(targ) ,σ_(v) _(targ) , σ_(v) _(host) , and σ_(w) _(host) are defaultinitialization values.
 6. The controller of claim 3, wherein the errorbound is based on inputs consisting of the current position and velocityof the target vehicle, the future time, the current velocity and yawrate of the host vehicle, and the relative heading angle between thehost and target vehicles.
 7. The controller of claim 1, wherein theprocessor is further programmed to calculate the predicted position atthe future time.
 8. The controller of claim 7, wherein the processor isprogrammed to generate coefficients to calculate the predicted positionat the future time using a quadratic polynomial method.
 9. Thecontroller of claim 7, wherein the processor is further programmed tocontrol a vehicle subsystem to evade the target vehicle based on thepredicted position and the error bound.
 10. The controller of claim 9,wherein controlling the vehicle subsystem includes instructing brakes tobrake.
 11. The controller of claim 9, wherein controlling the vehiclesubsystem includes instructing a steering system to turn.
 12. Thecontroller of claim 1, wherein the processor is further programmed tocalculate an error metric equal to an absolute value of a difference ofthe predicted position and a measured position at the future time of thetarget vehicle relative laterally to the current position andorientation of the host vehicle.
 13. The controller of claim 12, whereinthe processor is further programmed to calculate a quality measure equalto a minimum value during a period of a lesser value of 1 and a ratio ofthe error bound and the error metric.
 14. The controller of claim 13,wherein the processor is further programmed to disable a feature relyingon the error bound based on the quality measure.
 15. The controller ofclaim 14, wherein the feature is one of automated braking, automatedsteering, and autonomous operation.
 16. A method comprising: calculatingan error bound for confidence intervals of a predicted position at afuture time of a target vehicle relative laterally to a current positionand orientation of a host vehicle based on a current position andvelocity of the target vehicle and the future time.
 17. The method ofclaim 16, wherein the error bound is further based on a current velocityand yaw rate of the host vehicle and a relative heading angle betweenthe host and target vehicles.
 18. The method of claim 17, furthercomprising calculating an error metric equal to an absolute value of adifference of the predicted position and a measured position at thefuture time of the target vehicle relative laterally to the currentposition and orientation of the host vehicle.
 19. The method of claim18, further comprising calculating a quality measure equal to a minimumvalue during a period of a lesser value of 1 and a ratio of the errorbound and the error metric.
 20. The method of claim 19, furthercomprising adjusting a path prediction algorithm based on the qualitymeasure.